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authorredpony <redpony@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-10-01 20:13:48 +0000
committerredpony <redpony@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-10-01 20:13:48 +0000
commitde3b20fd379a62f8f381990f4d819a732b57a814 (patch)
treef4ee81303ad8d595224087ff6b753680b354ce88 /training/compute_cllh.cc
parent597db20b3b38fd0cbb3e3d9a7105b0c3c5c37e84 (diff)
compute obj, fixes for grammar filter
git-svn-id: https://ws10smt.googlecode.com/svn/trunk@668 ec762483-ff6d-05da-a07a-a48fb63a330f
Diffstat (limited to 'training/compute_cllh.cc')
-rw-r--r--training/compute_cllh.cc185
1 files changed, 185 insertions, 0 deletions
diff --git a/training/compute_cllh.cc b/training/compute_cllh.cc
new file mode 100644
index 00000000..f25e17c3
--- /dev/null
+++ b/training/compute_cllh.cc
@@ -0,0 +1,185 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <mpi.h>
+#include <boost/mpi.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "verbose.h"
+#include "hg.h"
+#include "prob.h"
+#include "inside_outside.h"
+#include "ff_register.h"
+#include "decoder.h"
+#include "filelib.h"
+#include "weights.h"
+
+using namespace std;
+namespace po = boost::program_options;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ opts.add_options()
+ ("weights,w",po::value<string>(),"Input feature weights file")
+ ("training_data,t",po::value<string>(),"Training data corpus")
+ ("decoder_config,c",po::value<string>(),"Decoder configuration file");
+ po::options_description clo("Command line options");
+ clo.add_options()
+ ("config", po::value<string>(), "Configuration file")
+ ("help,h", "Print this help message and exit");
+ po::options_description dconfig_options, dcmdline_options;
+ dconfig_options.add(opts);
+ dcmdline_options.add(opts).add(clo);
+
+ po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+ if (conf->count("config")) {
+ ifstream config((*conf)["config"].as<string>().c_str());
+ po::store(po::parse_config_file(config, dconfig_options), *conf);
+ }
+ po::notify(*conf);
+
+ if (conf->count("help") || !conf->count("training_data") || !conf->count("decoder_config")) {
+ cerr << dcmdline_options << endl;
+ MPI::Finalize();
+ exit(1);
+ }
+}
+
+void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c, vector<int>* ids) {
+ ReadFile rf(fname);
+ istream& in = *rf.stream();
+ string line;
+ int lc = 0;
+ while(in) {
+ getline(in, line);
+ if (!in) break;
+ if (lc % size == rank) {
+ c->push_back(line);
+ ids->push_back(lc);
+ }
+ ++lc;
+ }
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct TrainingObserver : public DecoderObserver {
+ void Reset() {
+ acc_obj = 0;
+ }
+
+ virtual void NotifyDecodingStart(const SentenceMetadata&) {
+ cur_obj = 0;
+ state = 1;
+ }
+
+ // compute model expectations, denominator of objective
+ virtual void NotifyTranslationForest(const SentenceMetadata&, Hypergraph* hg) {
+ assert(state == 1);
+ state = 2;
+ SparseVector<prob_t> cur_model_exp;
+ const prob_t z = InsideOutside<prob_t,
+ EdgeProb,
+ SparseVector<prob_t>,
+ EdgeFeaturesAndProbWeightFunction>(*hg, &cur_model_exp);
+ cur_obj = log(z);
+ }
+
+ // compute "empirical" expectations, numerator of objective
+ virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+ assert(state == 2);
+ state = 3;
+ SparseVector<prob_t> ref_exp;
+ const prob_t ref_z = InsideOutside<prob_t,
+ EdgeProb,
+ SparseVector<prob_t>,
+ EdgeFeaturesAndProbWeightFunction>(*hg, &ref_exp);
+
+ double log_ref_z;
+#if 0
+ if (crf_uniform_empirical) {
+ log_ref_z = ref_exp.dot(feature_weights);
+ } else {
+ log_ref_z = log(ref_z);
+ }
+#else
+ log_ref_z = log(ref_z);
+#endif
+
+ // rounding errors means that <0 is too strict
+ if ((cur_obj - log_ref_z) < kMINUS_EPSILON) {
+ cerr << "DIFF. ERR! log_model_z < log_ref_z: " << cur_obj << " " << log_ref_z << endl;
+ exit(1);
+ }
+ assert(!isnan(log_ref_z));
+ acc_obj += (cur_obj - log_ref_z);
+ }
+
+ double acc_obj;
+ double cur_obj;
+ int state;
+};
+
+namespace mpi = boost::mpi;
+
+int main(int argc, char** argv) {
+ mpi::environment env(argc, argv);
+ mpi::communicator world;
+ const int size = world.size();
+ const int rank = world.rank();
+ if (size > 1) SetSilent(true); // turn off verbose decoder output
+ register_feature_functions();
+
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+
+ // load initial weights
+ Weights weights;
+ if (conf.count("weights"))
+ weights.InitFromFile(conf["weights"].as<string>());
+
+ // freeze feature set
+ //const bool freeze_feature_set = conf.count("freeze_feature_set");
+ //if (freeze_feature_set) FD::Freeze();
+
+ // load cdec.ini and set up decoder
+ ReadFile ini_rf(conf["decoder_config"].as<string>());
+ Decoder decoder(ini_rf.stream());
+ if (decoder.GetConf()["input"].as<string>() != "-") {
+ cerr << "cdec.ini must not set an input file\n";
+ abort();
+ }
+
+ vector<string> corpus; vector<int> ids;
+ ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
+ assert(corpus.size() > 0);
+ assert(corpus.size() == ids.size());
+
+ vector<double> wv;
+ weights.InitVector(&wv);
+ decoder.SetWeights(wv);
+ TrainingObserver observer;
+ double objective = 0;
+ bool converged = false;
+
+ observer.Reset();
+ if (rank == 0)
+ cerr << "Each processor is decoding " << corpus.size() << " training examples...\n";
+
+ for (int i = 0; i < corpus.size(); ++i) {
+ decoder.SetId(ids[i]);
+ decoder.Decode(corpus[i], &observer);
+ }
+
+ reduce(world, observer.acc_obj, objective, std::plus<double>(), 0);
+
+ if (rank == 0)
+ cout << "OBJECTIVE: " << objective << endl;
+
+ return 0;
+}